Model-based Monte Carlo state estimation for condition-based component replacement

This paper presents a model-based Monte Carlo method, also called particle filtering, for estimating the failure probability of a component subject to degradation. The estimations are embedded within an optimal policy of condition-based component replacement, in which both replacement upon failure and preventive replacement are considered, and the decision variable is the expected cost per unit time. An application is reported with regards to a component subject to fatigue degradation, modeled by the well-known Paris–Erdogan law. The possibility of predicting accurately the component remaining life with the associated uncertainty and of updating the prediction on the basis of observations of the degradation process, opens the door for effective condition-based replacement planning and risk-informed life-extension for hazardous technologies, such as the nuclear, aerospace and chemical ones.